Many organizations use enterprise resource planning (ERP) systems to manage their databases. It is common that each of various sections in an organization developed and selected its own ERP system, suitable to the section to manage the database used by the section. Thus, within a single organization, various databases and ERP systems are used. In order to gain the overall business view, many organizations felt a need to have a system that integrates those ERP systems existing within the organizations.
One approach to this problem, also called data driven approach, is to extract data from the ERP systems, and build a data warehouse for the entire organization to store the extracted data. Managers of organizations use business intelligence tools to extract desired data from the data warehouse and view the data through reports. Business Intelligence tools expose a business view of available information and allow users to select information and format reports. While business intelligence tools typically provide predefined reports along with transaction processing systems, most business intelligence reports are custom written.
This data driven approach provides a simple solution in theory. In reality, however, it involves various problems in getting data from ERP systems, putting it in a data warehouse, and then getting the data out from the data warehouse. It is often difficult to get the right information to the right people at the right time. It is therefore desirable to provide a mechanism that makes it easier to extract data out of ERP systems and deliver it to the right people at the right time.
Existing data warehouses are customary made based on the existing ERP systems in individual organizations. It is a costly process that involves multiple specialists for many months to only create an application for a single one of many functional areas of the organization. Once a data warehouse is created, it is often difficult to adapt to changes.
In view of the problems relating to the data driven approach, it is proposed to adopt a model driven approach in managing data warehouses and business intelligence tools. In an article “A model-driven approach to Bl and data warehousing puts analytical power back in step with business requirements” (by Neil Raden; Intelligent Enterprise, The New Deal, March 2004), Raden proposes to use conceptual models based on metadata that describes transparent data structure. Extract-transform-load (ETL) processes are specified at a level of abstraction and directed at the conceptual models. Bl queries are framed in the conceptual models.
There exist some tools that employ the concept of the model driven approach as described in the article. However, those tools are limited to specific tasks, e.g., ETL. It is desirable to provide a packaged solution that allows efficient construction and management of both data warehouse and business intelligence capabilities.
In an article “The 38 Subsystems of ETL” (by Ralph Kimball; Intelligent Enterprise, December 2004), Kimball describes 38 subsystems that are needed in almost every ETL system in holding and maintaining a data warehouse. Existing ETL tools are not satisfactory at automating the best practice for implementation of those subsystems.
It is desirable to provide a mechanism that implements and substantiates the best practice as an integrated packaged system.